Noorfa Haszlinna Mustaffa

Firefly Algorithm for Optimization Problem

This paper reviews the applications of Firefly Algorithm (FA) in various domain of optimization problem. Optimization is a process of determining the best solution to make something as functional and effective as possible by minimizing or maximizing the parameters involved in the problems. Several categories of optimization problem such as discrete, chaotic, multi-objective and many more are addressed by inspiring the behavior of fireflies as mentioned in the literatures. Literatures found that FA was mostly applied by researchers to solve the optimization problems in Computer Science and Engineering domain. Some of them are enhanced or hybridized with other techniques to discover better performance. In addition, literatures found that most of the cases that used FA technique have outperformed compare to other metaheuristic algorithms.

The Conceptual Model of Uncertainty Factors in Environmental Issues on late Delivery for Construction Industry

Every industry keen to maximize their company profit by fulfils the customer’s satisfaction by perform a very good project deliverable. However, very limited researches on uncertainty in environmental issues (EI) probably could turn as a biggest problem for company, especially in late delivery of project completion for construction industry (CI). Uncertainty factors could be mapped by many causes and affects which known or unknown, statically to totally ignorance. Previously, most of research on uncertainty have been model their factors of uncertainties but pay no attention to the EI, whereas in the real cases all the factors must controlled and manageable even it is in-deterministic or non-realistic. Therefore, the modelling of uncertainty factors in EI on late delivery for CI is very important to studied, and it will be considered to be used as the guidance for decision makers when they are facing of the problems that related to uncertainties. This paper will purpose the conceptual model on progress to the modelling of uncertainty factors in EI on late delivery for CI. The uncertainty of environmental can split into two categories; acts of God and acts of humans.

The Mitsubishi MelfaRxm middleware and application: A case study of RV-2AJ robot

The main goal of this paper is reviewing the MelfaRxm middleware that was provided by Mitsubishi for their robot series. MelfaRxm provides the communication abstraction for software control development. Based on Windows platform and high level language, it can produce the real-time and offline programming for control robot. This middleware also supports for developing control multiple robots in one time. Furthermore, the experiment of the robot system has been developed to verify the functional of the middleware. It contains two sections of the interface. First is the control robot interface which presents the function at the robot control unit. Second is the operational interface which presents the manipulator control. Mitsubishi RV-2AJ robot has been chosen as a study case. Finally future work and conclusion will be elaborated.

Optimization of Surface Roughness in Turning Operation Using Firefly Algorithm

Recently, Firefly Algorithm (FA) has become an important technique to solve optimization problems. Various FA variants have been developed to suit various applications. In this paper, FA is used to optimize machining parameters such as % Volume fraction of SiC (V), cutting speed (S), feed rate (F), depth of cut (D) and machining time (T). The optimal machining cutting parameters estimated by FA that lead to a minimum surface roughness are validated using ANOVA test.

Supply Chain Simulation and Optimization Methods: An Overview

This paper reviews a simulation method in the context of supply chain management (SCM). SCM problem usually are complex and need some effective method like a simulation for modeling. This study focuses on simulation modeling and tools that have been discussed and practiced by previous researchers, practitioners and also academics. The used of simulation tools can be classified into three groups: spreadsheet simulation, simulation software package and simulation programming language. Simulation is the best practice to evaluate the system performance closely to real situation. However, simulation solely is not enough to achieve optimal result and need to corporate with optimization techniques which also discussed in this paper. The overviews of simulation optimization and its application in SCM fields are also addressed. Lastly, several suggestions for future research were stated in conclusion section.

Metaheuristic simulation optimisation for the stochastic multi-retailer supply chain

Supply Chain Management (SCM) is an important activity in all producing facilities and in many organizations to enable vendors, manufacturers and suppliers to interact gainfully and plan optimally their flow of goods and services. A simulation optimization approach has been widely used in research nowadays on finding the best solution for decision-making process in Supply Chain Management (SCM) that generally faced a complexity with large sources of uncertainty and various decision factors. Metahueristic method is the most popular simulation optimization approach. However, very few researches have applied this approach in optimizing the simulation model for supply chains. Thus, this paper interested in evaluating the performance of metahueristic method for stochastic supply chains in determining the best flexible inventory replenishment parameters that minimize the total operating cost. The simulation optimization model is proposed based on the Bees algorithm (BA) which has been widely applied in engineering application such as training neural networks for pattern recognition. BA is a new member of meta-heuristics. BA tries to model natural behavior of honey bees in food foraging. Honey bees use several mechanisms like waggle dance to optimally locate food sources and to search new ones. This makes them a good candidate for developing new algorithms for solving optimization problems. This model considers an outbound centralised distribution system consisting of one supplier and 3 identical retailers and is assumed to be independent and identically distributed with unlimited supply capacity at supplier.

Exploratory study on the effect of discount pricing strategies for new product introduction

Rapid introduction of new product into the market has resulted in growing competition between retailers. Nowadays, retailers compete with one another in order to increase revenue and to maintain their position in the marketplace. This situation has forced the retailers to enhance their strategic management as well as creating competitive advantages. Generally, this situation can be observed in highly demanded product such as fashion goods and high technology electronic devices (smart phone, notebook). The consequence from the intense competition and new product introduction is difficulties in retailers pricing management. Retailers are now facing with complexity in making decisions on suitable pricing strategies and discount level for new product in association with the product life cycle. Thus, this research aims to investigate the suitable discount pricing strategies that can be integrated in every phase of product life cycle. This paper presents relationships between the discount pricing and the stages in the product life cycle in the form of conceptual diagram and mathematical expression. A system dynamic approach is used for developing the conceptual diagram and formulating the mathematical expression for the discount pricing strategies to visualize the relationship between discount pricing and product life cycle.

A study of metaheuristic algorithms for high dimensional feature selection on microarray data

Microarray systems enable experts to examine gene profile at molecular level using machine learning algorithms. It increases the potentials of classification and diagnosis of many diseases at gene expression level. Though, numerous difficulties may affect the efficiency of machine learning algorithms which includes vast number of genes features comprised in the original data. Many of these features may be unrelated to the intended analysis. Therefore, feature selection is necessary to be performed in the data pre-processing. Many feature selection algorithms are developed and applied on microarray which including the metaheuristic optimization algorithms. This paper discusses the application of the metaheuristics algorithms for feature selection in microarray dataset. This study reveals that, the algorithms have yield an interesting result with limited resources thereby saving computational expenses of machine learning algorithms.

Machining Parameters Optimization using Hybrid Firefly Algorithm and Particle Swarm Optimization

Firefly Algorithm (FA) is a metaheuristic algorithm that is inspired by the flashing behavior of fireflies and the phenomenon of bioluminescent communication and the algorithm is used to optimize the machining parameters (feed rate, depth of cut, and spindle speed) in this research. The algorithm is hybridized with Particle Swarm Optimization (PSO) to discover better solution in exploring the search space. Objective function of previous research is used to optimize the machining parameters in turning operation. The optimal machining cutting parameters estimated by FA that lead to a minimum surface roughness are validated using ANOVA test.

Police personality classification using principle component analysis-artificial neural network

Personality is the defining essence of an individual as it guides the way we think, act and interpret external stimuli. Classification of personality is important as it can serves as a framework in the job assignment task, particularly, in the high risk job including the Police Force. There are many attributes of individual traits but not all of them can be used to indicate individual personality. In this paper, two classification models were developed to predict individual personality for the Royal Malaysian police (RMP) based on Type A and Type B personality theory. Both classification models are based on Artificial Neural Network (ANN). But, the second model applied Principle Component Analysis or called as PCA-ANN model. The second classification model successfully reduces the number of personality features to six features compared to initial 10 features. Furthermore, PCA-ANN improves the classification accuracy to 98.6% compared to 94.4% classification accuracy found in the first ANN model.